AI-Powered Safeguards for NB-IoT: An Experimental Analysis of Security Models against Botnet Attacks

Abstract

Narrowband Internet of Things (NB-IoT)has become a prominent technology in communication in both large scale low-power IoT deployment in real-time (e.g., smart cities, health monitoring), and automation in industries (e.g.,automated guided vehicles (AGVs), AI-driven inventory systems). Although NB-IoT is efficient and widely covered, it is susceptible to changing cyber-threats such as botnet attacks that aggressively utilize resource-constrained devices and centralized network architecture. The conventional security systems, the largest part of which relies heavily on fixed rules and signature-driven identification, cannot identify advanced and dynamic attack patterns on the fly. In this regard, the purpose of this paper is to comment on the application of AI in enhancing security systems in NB-IoT networks. To explore the AI security models, the paper explores efficient algorithms such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Long Short Term Model (LSTM), and Support Vector Machine (SVM) in learning how a network behaves, how to detect anomalies, and how to react proactively to intrusion through botnets. The machine learning and deep learning models will be compared in terms of identifying malicious traffic, affected devices, and coordinated attack patterns to ensure higher detection and resilience at reduced latency. We demonstrate that AI-based defences are much more effective in detection and resiliency than conventional ones, without perceptibly reducing the low power and low-bandwidth characteristics of NB-IoT systems. This paper emphasizes the necessity of smart and dynamically adaptable security solutions in real time and provides insight into how secure and resilient IoT Infrastructures can be developed to address large-scale botnet attacks in the future.